Driving the refactoring of Java Enterprise applications by evaluating the distance between application elements

Java Enterprise Applications (JEAs) are complex systems composed using various technologies that in turn rely on languages other than Java, such as XML or SQL. Given the complexity of these applications, the need to reverse engineer them in order to support further development becomes critical. In this paper we show how it is possible to split a system into layers and how is possible to interpret the distance between application elements in order to support the refactoring of JEAs. The purpose of this paper is to explore ways to provide suggestions about the refactoring operations to perform on the code by evaluating the distance between layers and elements belonging those layers. We split JEAs into layers by considering the kinds and the purposes of the elements composing the application. We measure distance between elements by using the notion of the shortest path in a graph. Also we present how to enrich the interpretation of the distance value with enterprise pattern detection in order to refine the suggestion about modifications to perform on the code.

Semantic Methods for Intelligent Distributed Design Environments

Continuous advancements in technology have led to increasingly comprehensive and distributed product development processes while in pursuit of improved products at reduced costs. Information associated with these products is ever changing, and structured frameworks have become integral to managing such fluid information. Ontologies and the Semantic Web have emerged as key alternatives for capturing product knowledge in both a human-readable and computable manner. The primary and conclusive focus of this research is to characterize relationships formed within methodically developed distributed design knowledge frameworks to ultimately provide a pervasive real-time awareness in distributed design processes. Utilizing formal logics in the form of the Semantic Web’s OWL and SWRL, causal relationships are expressed to guide and facilitate knowledge acquisition as well as identify contradictions between knowledge in a knowledge base. To improve the efficiency during both the development and operational phases of these “intelligent” frameworks, a semantic relatedness algorithm is designed specifically to identify and rank underlying relationships within product development processes. After reviewing several semantic relatedness measures, three techniques, including a novel meronomic technique, are combined to create AIERO, the Algorithm for Identifying Engineering Relationships in Ontologies. In determining its applicability and accuracy, AIERO was applied to three separate, independently developed ontologies. The results indicate AIERO is capable of consistently returning relatedness values one would intuitively expect. To assess the effectiveness of AIERO in exposing underlying causal relationships across product development platforms, a case study involving the development of an industry-inspired printed circuit board (PCB) is presented. After instantiating the PCB knowledge base and developing an initial set of rules, FIDOE, the Framework for Intelligent Distributed Ontologies in Engineering, was employed to identify additional causal relationships through extensional relatedness measurements. In a conclusive PCB redesign, the resulting “intelligent” framework demonstrates its ability to pass values between instances, identify inconsistencies amongst instantiated knowledge, and identify conflicting values within product development frameworks. The results highlight how the introduced semantic methods can enhance the current knowledge acquisition, knowledge management, and knowledge validation capabilities of traditional knowledge bases.

Enterprise Integration of Smart Objects using Semantic Service Descriptions

Integrating physical objects (smart objects) and enterprise IT systems is still a labor intensive, mainly manual task done by domain experts. On one hand, enterprise IT backend systems are based on service oriented architectures (SOA) and driven by business rule engines or business process execution engines. Smart objects on the other hand are often programmed at very low levels. In this paper we describe an approach that makes the integration of smart objects with such backends systems easier. We introduce semantic endpoint descriptions based on Linked USDL. Furthermore, we show how different communication patterns can be integrated into these endpoint descriptions. The strength of our endpoint descriptions is that they can be used to automatically create REST or SOAP endpoints for enterprise systems, even if which they are not able to talk to the smart objects directly. We evaluate our proposed solution with CoAP, UDP and 6LoWPAN, as we anticipate the industry converge towards these standards. Nonetheless, our approach also allows easy integration with backend systems, even if no standardized protocol is used.

Integration of WSNs into enterprise systems based on semantic physical business entities

Internet of Things based systems are anticipated to gain widespread use in industrial applications. Standardization efforts, like 6L0WPAN and the Constrained Application Protocol (CoAP) have made the integration of wireless sensor nodes possible using Internet technology and web-like access to data (RESTful service access). While there are still some open issues, the interoperability problem in the lower layers can now be considered solved from an enterprise software vendors' point of view. One possible next step towards integration of real-world objects into enterprise systems and solving the corresponding interoperability problems at higher levels is to use semantic web technologies. We introduce an abstraction of real-world objects, called Semantic Physical Business Entities (SPBE), using Linked Data principles. We show that this abstraction nicely fits into enterprise systems, as SPBEs allow a business object centric view on real-world objects, instead of a pure device centric view. The interdependencies between how currently services in an enterprise system are used and how this can be done in a semantic real-world aware enterprise system are outlined, arguing for the need of semantic services and semantic knowledge repositories. We introduce a lightweight query language, which we use to perform a quantitative analysis of our approach to demonstrate its feasibility.

Towards an Emergent Semantic Web

In his in uential article about the evolution of the Web, Berners-Lee [1] envisions a Semantic Web in which humans and computers alike are capable of understanding and processing information. This vision is yet to materialize. The main obstacle for the Semantic Web vision is that in today's Web meaning is rooted most often not in formal semantics, but in natural language and, in the sense of semiology, emerges not before interpretation and processing. Yet, an automated form of interpretation and processing can be tackled by precisiating raw natural language. To do that, Web agents extract fuzzy grassroots ontologies through induction from existing Web content. Inductive fuzzy grassroots ontologies thus constitute organically evolved knowledge bases that resemble automated gradual thesauri, which allow precisiating natural language [2]. The Web agents' underlying dynamic, self-organizing, and best-effort induction, enable a sub-syntactical bottom up learning of semiotic associations. Thus, knowledge is induced from the users' natural use of language in mutual Web interactions, and stored in a gradual, thesauri-like lexical-world knowledge database as a top-level ontology, eventually allowing a form of computing with words [3]. Since when computing with words the objects of computation are words, phrases and propositions drawn from natural languages, it proves to be a practical notion to yield emergent semantics for the Semantic Web. In the end, an improved understanding by computers on the one hand should upgrade human- computer interaction on the Web, and, on the other hand allow an initial version of human- intelligence amplification through the Web.

A fuzzy grassroots ontology for improving social semantic web search

The web is continuously evolving into a collection of many data, which results in the interest to collect and merge these data in a meaningful way. Based on that web data, this paper describes the building of an ontology resting on fuzzy clustering techniques. Through continual harvesting folksonomies by web agents, an entire automatic fuzzy grassroots ontology is built. This self-updating ontology can then be used for several practical applications in fields such as web structuring, web searching and web knowledge visualization.A potential application for online reputation analysis, added value and possible future studies are discussed in the conclusion.

Prometheus Framework for Fuzzy Information Retrieval in Semantic Spaces

This paper introduces a novel vision for further enhanced Internet of Things services. Based on a variety of data (such as location data, ontology-backed search queries, in- and outdoor conditions) the Prometheus framework is intended to support users with helpful recommendations and information preceding a search for context-aware data. Adapted from artificial intelligence concepts, Prometheus proposes user-readjusted answers on umpteen conditions. A number of potential Prometheus framework applications are illustrated. Added value and possible future studies are discussed in the conclusion.

Prometheus – Fuzzy Information Retrieval for Semantic Homes and Environments

This paper introduces a novel vision for further enhanced Internet of Things services. Based on a variety of data – such as location data, ontology-backed search queries, in- and outdoor conditions – the Prometheus framework is intended to support users with helpful recommendations and information preceding a search for context-aware data. Adapted from artificial intelligence concepts, Prometheus proposes user-readjusted answers on umpteen conditions. A number of potential Prometheus framework applications are illustrated. Added value and possible future studies are discussed in the conclusion.

Semantic Representation of Synaesthesia

Synaesthesia has multifaceted consequences for both subjective experience and cognitive performance. Here, I broach the issue of how synaesthesia is represented in semantic memory. I hypothesize that, for example, in grapheme colour synaesthesia, colour is represented as an additional feature in the semantic network that enables the formation of associations that are not present in non-synaesthetes. Thus, synaesthesia provokes richer memory representations which enable learning opportunities that are not present in non-synaesthetes, provides additional memory cues, and may trigger creative ideas.